LangGraph Mastery: Build AI Agents, RAG & Multi-Agent System

Build AI agents using LangGraph. Learn ReAct agents, Agentic RAG, multi-agent systems, memory & human-in-the-loop apps

What you’ll learn
✓ Build production-ready AI agents using LangGraph and understand how nodes, edges, and state management create intelligent AI workflows.
✓ Design ReAct agents that reason, take actions, and use external tools to solve complex tasks autonomously.
✓ Implement memory systems in AI agents so they can retain conversation history and context across interactions
✓ Create Agentic RAG systems where AI decides when to retrieve information, evaluate results, and generate accurate responses.
✓ Develop multi-agent systems where specialized AI agents collaborate to solve complex problems.
✓ Build human-in-the-loop AI systems where humans review and approve critical decisions made by AI agents.
✓ Design intelligent AI workflows using LangGraph loops, routing, and conditional logic.
✓ Integrate tools, APIs, and search systems into AI agents to create powerful real-world applications
✓ Understand how modern AI products use agentic architectures such as RAG pipelines and multi-agent collaboration.
✓ Build multiple mini projects that demonstrate real-world AI systems used in modern AI engineering.

Requirements
● Basic knowledge of Python programming (variables, functions, and simple scripts).
● Basic understanding of programming concepts such as loops, conditions, and functions.
● A computer with internet access to install Python and run the provided code examples
● Curiosity to learn modern AI technologies and build real-world AI systems.
● No prior experience with LangGraph, AI agents, or RAG systems is required — everything will be explained step by step.
● Familiarity with Python libraries such as pip and virtual environments is helpful but not mandatory.

Description
Artificial Intelligence is evolving rapidly, and the next generation of AI systems is no longer built with simple prompts. Modern AI applications rely on AI agents, multi-agent collaboration, retrieval-augmented generation (RAG), and intelligent workflows.

This course is designed to teach you how to build those systems from scratch using LangGraph.

LangGraph is one of the most powerful frameworks for building agentic AI systems, allowing developers to create workflows where AI can reason, make decisions, use tools, retrieve knowledge, and collaborate with other agents.

In this course, you will learn how to design and build production-ready AI systems, not just basic examples.

Unlike many tutorials that focus only on theory, this course follows a project-based learning approach. Each major section includes a hands-on mini project where you will implement the concepts and build working AI systems.

By the end of the course, you will have built multiple real-world AI applications, including intelligent agents, multi-agent systems, and advanced RAG pipelines.

Why Learn LangGraph?

Traditional AI applications rely on simple chains of prompts and responses. However, modern AI systems require structured workflows and intelligent decision making.

LangGraph enables developers to build agentic AI systems where AI models can

• Decide which action to take next

• Use external tools and APIs

• Retrieve knowledge from data sources

• Collaborate with other agents

• Remember previous interactions

• Involve humans in critical decisions

This makes LangGraph one of the most important tools for developers building advanced AI applications.

What You Will Learn

In this course, you will learn how to build intelligent AI systems using LangGraph and modern AI engineering techniques.

You will learn

• The fundamentals of LangGraph workflows

• How to design AI systems using nodes, edges, and state management

• How to build ReAct agents that reason and act

• How to implement memory systems for AI applications

• How to create looping workflows that improve responses

• How to design Agentic RAG systems with intelligent retrieval

• How to build multi-agent systems where agents collaborate

• How to implement human-in-the-loop AI workflows

• How to integrate tools and APIs into AI agents

• How to design scalable AI architectures

These are the same types of architectures used in modern AI products and enterprise systems.

Project-Based Learning (Mini Projects)

This course focuses heavily on practical implementation.

Instead of only learning theory, you will build real mini projects throughout the course.

You will build systems such as

• AI agents that use tools to solve tasks

• Decision-making AI workflows using LangGraph

• ReAct agents capable of reasoning and acting

• AI workflows with memory and context retention

• Multi-tool AI agents combining search and computation

• A complete Agentic RAG system

• A multi-agent collaboration system

• A human-in-the-loop AI system for safe decision making

These projects will help you understand how modern AI systems are built in practice.

Who this course is for
■ Python developers who want to learn how to build AI agents and intelligent AI systems using LangGraph.
■ Machine learning and AI enthusiasts interested in building modern agentic AI systems such as ReAct agents, RAG pipelines, and multi-agent workflows
■ Developers who want to move beyond prompt engineering and learn how real AI applications are built using workflows and agent architectures.
■ Engineers and software developers exploring Generative AI and looking to build practical AI applications with tools, APIs, and retrieval systems.
■ Students and professionals who want hands-on experience building AI agents, Agentic RAG systems, and multi-agent AI workflows.
■ Anyone curious about how modern AI products are built using frameworks like LangGraph and LangChain

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